Suggesting queries based upon keywords

ABSTRACT

One or more computing devices, systems, and/or methods for generating a list of suggested queries associated with one or more keywords are provided. For example, one or more keywords may be received via a search interface. A plurality of queries associated with the one or more keywords may be determined based upon the one or more keywords and a historical query database. A plurality of relationship scores associated with the plurality of queries may be generated based upon a plurality of search sessions associated with the historical query database. The historical query database may be analyzed to determine a plurality of click rates associated with the plurality of queries. A list of suggested queries may be generated based upon the plurality of relationship scores and the plurality of click rates.

BACKGROUND

Many services, such as websites, applications, etc. may provideplatforms for performing searches. For example, a user may interact witha search interface. The user may use the search interface to perform asearch based upon a query. The search interface may determine one ormore queries related to the query and/or suggest the one or more queriesto the user.

SUMMARY

In accordance with the present disclosure, one or more computing devicesand/or methods are provided. In an example, a graphical user interfaceof a first device may be controlled to display a search interface. Oneor more keywords may be received, via the search interface, from thefirst device. A plurality of queries associated with the one or morekeywords may be determined based upon the one or more keywords and/or ahistorical query database comprising a plurality of historical queries.A plurality of relationship scores associated with the plurality ofqueries may be generated based upon a plurality of relationship scoresassociated with the plurality of historical queries. A first searchsession of the plurality of search sessions corresponds to one or moresearches performed via a second device using one or more queries of theplurality of historical queries. A relationship score of the pluralityof relationship scores is associated with a relationship between a queryof the plurality of queries and the one or more keywords. The historicalquery database may be analyzed to determine a plurality of click ratesassociated with the plurality of queries. A list of suggested queriesassociated with the one or more keywords may be generated based upon theplurality of queries, the plurality of relationship scores and/or theplurality of click rates.

DESCRIPTION OF THE DRAWINGS

While the techniques presented herein may be embodied in alternativeforms, the particular embodiments illustrated in the drawings are only afew examples that are supplemental of the description provided herein.These embodiments are not to be interpreted in a limiting manner, suchas limiting the claims appended hereto.

FIG. 1 is an illustration of a scenario involving various examples ofnetworks that may connect servers and clients.

FIG. 2 is an illustration of a scenario involving an exampleconfiguration of a server that may utilize and/or implement at least aportion of the techniques presented herein.

FIG. 3 is an illustration of a scenario involving an exampleconfiguration of a client that may utilize and/or implement at least aportion of the techniques presented herein.

FIG. 4 is a flow chart illustrating an example method for generating alist of suggested queries associated with one or more keywords.

FIG. 5A is a component block diagram illustrating an example system forgenerating a list of suggested queries associated with one or morekeywords, where a search interface is presented and/or accessed by afirst device.

FIG. 5B is a component block diagram illustrating an example system forgenerating a list of suggested queries associated with one or morekeywords, where a plurality of query paths associated with one or morefirst keywords are determined.

FIG. 5C is a component block diagram illustrating an example system forgenerating a list of suggested queries associated with one or morekeywords, where a first list of search results associated with one ormore first keywords and/or a first list of suggested queries associatedwith the one or more first keywords are presented by a first device.

FIG. 5D is a component block diagram illustrating an example system forgenerating a list of suggested queries associated with one or morekeywords, where a second list of search results associated with a firstquery and/or a second list of suggested queries associated with thefirst query are presented by a first device.

FIG. 5E is a component block diagram illustrating an example system forgenerating a list of suggested queries associated with one or morekeywords, where a third list of search results associated with a thirdquery and/or a third list of suggested queries associated with the thirdquery are presented by a first device.

FIG. 5F is a component block diagram illustrating an example system forgenerating a list of suggested queries associated with one or morekeywords, where a second web page is presented by a first deviceresponsive to a selection of a fifth search result.

FIG. 6 is an illustration of a scenario featuring an examplenon-transitory machine readable medium in accordance with one or more ofthe provisions set forth herein.

DETAILED DESCRIPTION

Subject matter will now be described more fully hereinafter withreference to the accompanying drawings, which form a part hereof, andwhich show, by way of illustration, specific example embodiments. Thisdescription is not intended as an extensive or detailed discussion ofknown concepts. Details that are known generally to those of ordinaryskill in the relevant art may have been omitted, or may be handled insummary fashion.

The following subject matter may be embodied in a variety of differentforms, such as methods, devices, components, and/or systems.Accordingly, this subject matter is not intended to be construed aslimited to any example embodiments set forth herein. Rather, exampleembodiments are provided merely to be illustrative. Such embodimentsmay, for example, take the form of hardware, software, firmware or anycombination thereof.

1. Computing Scenario

The following provides a discussion of some types of computing scenariosin which the disclosed subject matter may be utilized and/orimplemented.

1.1. Networking

FIG. 1 is an interaction diagram of a scenario 100 illustrating aservice 102 provided by a set of servers 104 to a set of client devices110 via various types of networks. The servers 104 and/or client devices110 may be capable of transmitting, receiving, processing, and/orstoring many types of signals, such as in memory as physical memorystates.

The servers 104 of the service 102 may be internally connected via alocal area network 106 (LAN), such as a wired network where networkadapters on the respective servers 104 are interconnected via cables(e.g., coaxial and/or fiber optic cabling), and may be connected invarious topologies (e.g., buses, token rings, meshes, and/or trees). Theservers 104 may be interconnected directly, or through one or more othernetworking devices, such as routers, switches, and/or repeaters. Theservers 104 may utilize a variety of physical networking protocols(e.g., Ethernet and/or Fiber Channel) and/or logical networkingprotocols (e.g., variants of an Internet Protocol (IP), a TransmissionControl Protocol (TCP), and/or a User Datagram Protocol (UDP). The localarea network 106 may include, e.g., analog telephone lines, such as atwisted wire pair, a coaxial cable, full or fractional digital linesincluding T1, T2, T3, or T4 type lines, Integrated Services DigitalNetworks (ISDNs), Digital Subscriber Lines (DSLs), wireless linksincluding satellite links, or other communication links or channels,such as may be known to those skilled in the art. The local area network106 may be organized according to one or more network architectures,such as server/client, peer-to-peer, and/or mesh architectures, and/or avariety of roles, such as administrative servers, authenticationservers, security monitor servers, data stores for objects such as filesand databases, business logic servers, time synchronization servers,and/or front-end servers providing a user-facing interface for theservice 102.

Likewise, the local area network 106 may comprise one or moresub-networks, such as may employ differing architectures, may becompliant or compatible with differing protocols and/or may interoperatewithin the local area network 106. Additionally, a variety of local areanetworks 106 may be interconnected; e.g., a router may provide a linkbetween otherwise separate and independent local area networks 106.

In the scenario 100 of FIG. 1, the local area network 106 of the service102 is connected to a wide area network 108 (WAN) that allows theservice 102 to exchange data with other services 102 and/or clientdevices 110. The wide area network 108 may encompass variouscombinations of devices with varying levels of distribution andexposure, such as a public wide-area network (e.g., the Internet) and/ora private network (e.g., a virtual private network (VPN) of adistributed enterprise).

In the scenario 100 of FIG. 1, the service 102 may be accessed via thewide area network 108 by a user 112 of one or more client devices 110,such as a portable media player (e.g., an electronic text reader, anaudio device, or a portable gaming, exercise, or navigation device); aportable communication device (e.g., a camera, a phone, a wearable or atext chatting device); a workstation; and/or a laptop form factorcomputer. The respective client devices 110 may communicate with theservice 102 via various connections to the wide area network 108. As afirst such example, one or more client devices 110 may comprise acellular communicator and may communicate with the service 102 byconnecting to the wide area network 108 via a wireless local areanetwork 106 provided by a cellular provider. As a second such example,one or more client devices 110 may communicate with the service 102 byconnecting to the wide area network 108 via a wireless local areanetwork 106 provided by a location such as the user's home or workplace(e.g., a WiFi (Institute of Electrical and Electronics Engineers (IEEE)Standard 802.11) network or a Bluetooth (IEEE Standard 802.15.1)personal area network). In this manner, the servers 104 and the clientdevices 110 may communicate over various types of networks. Other typesof networks that may be accessed by the servers 104 and/or clientdevices 110 include mass storage, such as network attached storage(NAS), a storage area network (SAN), or other forms of computer ormachine readable media.

1.2. Server Configuration

FIG. 2 presents a schematic architecture diagram 200 of a server 104that may utilize at least a portion of the techniques provided herein.Such a server 104 may vary widely in configuration or capabilities,alone or in conjunction with other servers, in order to provide aservice such as the service 102.

The server 104 may comprise one or more processors 210 that processinstructions. The one or more processors 210 may optionally include aplurality of cores; one or more coprocessors, such as a mathematicscoprocessor or an integrated graphical processing unit (GPU); and/or oneor more layers of local cache memory. The server 104 may comprise memory202 storing various forms of applications, such as an operating system204; one or more server applications 206, such as a hypertext transportprotocol (HTTP) server, a file transfer protocol (FTP) server, or asimple mail transport protocol (SMTP) server; and/or various forms ofdata, such as a database 208 or a file system. The server 104 maycomprise a variety of peripheral components, such as a wired and/orwireless network adapter 214 connectible to a local area network and/orwide area network; one or more storage components 216, such as a harddisk drive, a solid-state storage device (SSD), a flash memory device,and/or a magnetic and/or optical disk reader.

The server 104 may comprise a mainboard featuring one or morecommunication buses 212 that interconnect the processor 210, the memory202, and various peripherals, using a variety of bus technologies, suchas a variant of a serial or parallel AT Attachment (ATA) bus protocol; aUniform Serial Bus (USB) protocol; and/or Small Computer SystemInterface (SCI) bus protocol. In a multibus scenario, a communicationbus 212 may interconnect the server 104 with at least one other server.Other components that may optionally be included with the server 104(though not shown in the schematic diagram 200 of FIG. 2) include adisplay; a display adapter, such as a graphical processing unit (GPU);input peripherals, such as a keyboard and/or mouse; and a flash memorydevice that may store a basic input/output system (BIOS) routine thatfacilitates booting the server 104 to a state of readiness.

The server 104 may operate in various physical enclosures, such as adesktop or tower, and/or may be integrated with a display as an“all-in-one” device. The server 104 may be mounted horizontally and/orin a cabinet or rack, and/or may simply comprise an interconnected setof components. The server 104 may comprise a dedicated and/or sharedpower supply 218 that supplies and/or regulates power for the othercomponents. The server 104 may provide power to and/or receive powerfrom another server and/or other devices. The server 104 may comprise ashared and/or dedicated climate control unit 220 that regulates climateproperties, such as temperature, humidity, and/or airflow. Many suchservers 104 may be configured and/or adapted to utilize at least aportion of the techniques presented herein.

1.3. Client Device Configuration

FIG. 3 presents a schematic architecture diagram 300 of a client device110 whereupon at least a portion of the techniques presented herein maybe implemented. Such a client device 110 may vary widely inconfiguration or capabilities, in order to provide a variety offunctionality to a user such as the user 112. The client device 110 maybe provided in a variety of form factors, such as a desktop or towerworkstation; an “all-in-one” device integrated with a display 308; alaptop, tablet, convertible tablet, or palmtop device; a wearable devicemountable in a headset, eyeglass, earpiece, and/or wristwatch, and/orintegrated with an article of clothing; and/or a component of a piece offurniture, such as a tabletop, and/or of another device, such as avehicle or residence. The client device 110 may serve the user in avariety of roles, such as a workstation, kiosk, media player, gamingdevice, and/or appliance.

The client device 110 may comprise one or more processors 310 thatprocess instructions. The one or more processors 310 may optionallyinclude a plurality of cores; one or more coprocessors, such as amathematics coprocessor or an integrated graphical processing unit(GPU); and/or one or more layers of local cache memory. The clientdevice 110 may comprise memory 301 storing various forms ofapplications, such as an operating system 303; one or more userapplications 302, such as document applications, media applications,file and/or data access applications, communication applications such asweb browsers and/or email clients, utilities, and/or games; and/ordrivers for various peripherals. The client device 110 may comprise avariety of peripheral components, such as a wired and/or wirelessnetwork adapter 306 connectible to a local area network and/or wide areanetwork; one or more output components, such as a display 308 coupledwith a display adapter (optionally including a graphical processing unit(GPU)), a sound adapter coupled with a speaker, and/or a printer; inputdevices for receiving input from the user, such as a keyboard 311, amouse, a microphone, a camera, and/or a touch-sensitive component of thedisplay 308; and/or environmental sensors, such as a global positioningsystem (GPS) receiver 319 that detects the location, velocity, and/oracceleration of the client device 110, a compass, accelerometer, and/orgyroscope that detects a physical orientation of the client device 110.Other components that may optionally be included with the client device110 (though not shown in the schematic architecture diagram 300 of FIG.3) include one or more storage components, such as a hard disk drive, asolid-state storage device (SSD), a flash memory device, and/or amagnetic and/or optical disk reader; and/or a flash memory device thatmay store a basic input/output system (BIOS) routine that facilitatesbooting the client device 110 to a state of readiness; and a climatecontrol unit that regulates climate properties, such as temperature,humidity, and airflow.

The client device 110 may comprise a mainboard featuring one or morecommunication buses 312 that interconnect the processor 310, the memory301, and various peripherals, using a variety of bus technologies, suchas a variant of a serial or parallel AT Attachment (ATA) bus protocol;the Uniform Serial Bus (USB) protocol; and/or the Small Computer SystemInterface (SCI) bus protocol. The client device 110 may comprise adedicated and/or shared power supply 318 that supplies and/or regulatespower for other components, and/or a battery 304 that stores power foruse while the client device 110 is not connected to a power source viathe power supply 318. The client device 110 may provide power to and/orreceive power from other client devices.

In some scenarios, as a user 112 interacts with a software applicationon a client device 110 (e.g., an instant messenger and/or electronicmail application), descriptive content in the form of signals or storedphysical states within memory (e.g., an email address, instant messengeridentifier, phone number, postal address, message content, date, and/ortime) may be identified. Descriptive content may be stored, typicallyalong with contextual content. For example, the source of a phone number(e.g., a communication received from another user via an instantmessenger application) may be stored as contextual content associatedwith the phone number. Contextual content, therefore, may identifycircumstances surrounding receipt of a phone number (e.g., the date ortime that the phone number was received), and may be associated withdescriptive content. Contextual content, may, for example, be used tosubsequently search for associated descriptive content. For example, asearch for phone numbers received from specific individuals, receivedvia an instant messenger application or at a given date or time, may beinitiated. The client device 110 may include one or more servers thatmay locally serve the client device 110 and/or other client devices ofthe user 112 and/or other individuals. For example, a locally installedwebserver may provide web content in response to locally submitted webrequests. Many such client devices 110 may be configured and/or adaptedto utilize at least a portion of the techniques presented herein.

2. Presented Techniques

One or more computing devices and/or techniques are provided forgenerating a list of suggested queries associated with one or morekeywords are provided. For example, a user may access and/or interactwith a search interface that generates search results based uponqueries. The search interface may be an internal website searchinterface designed to search for information within a single website.Alternatively and/or additionally, the search interface may be a websearch engine designed to search for information throughout theInternet. Alternatively and/or additionally, the search interface may bea shopping search engine designed to search for product informationand/or service information within the single website and/or throughoutwebsites of the Internet.

The user may input a first query into the search interface and/or afirst plurality of search results may be generated based upon the firstquery. One or more suggested queries may be determined based upon thefirst query. The one or more suggested queries may be presented via thesearch interface. However, the one or more suggested queries may not beassociated with informative queries. An informative query may correspondto a query yielding search results that may provide the user withbackground information associated with a subject of the first query. Forexample, the subject of the first query may correspond to “smartphone”(e.g., the first query may comprise “smartphone”) and/or the backgroundinformation associated with the subject may correspond to a range ofsmartphones, average prices of smartphones, a number of vendors of oneor more smartphones, popularities of different smartphones, smartphonecompanies, etc. Rather than determining one or more informative queriesassociated with the first query, the one or more suggested queries maybe determined by predicting, based upon historical queries, one or morefinal stage queries associated with the first query in order to minimizea search session length associated with the user (e.g., the one or moresuggested queries may comprise one or more specific smartphone modelsand/or products which may not yield search results that provide the userwith background information associated with the subject of the firstquery). As a result of the one or more suggested queries not beingassociated with informative queries, the user may not interact with theone or more suggested queries and/or the one or more suggested queriesmay not help the user achieve an understanding of the subject of thefirst query.

Thus, in accordance with one or more of the techniques presented herein,a plurality of queries associated with the first query may be determinedbased upon the first query and/or a historical query database. In someexamples, a plurality of search sessions of the historical queriesand/or a plurality of sequences of queries associated with the pluralityof search sessions may be analyzed to generate a plurality ofrelationship scores associated with the plurality of queries. A list ofsuggested queries associated with the first query may be generated basedupon the plurality of queries and/or the plurality of relationshipscores. In some examples, the list of suggested queries may comprise oneor more informative queries as a result of the list of suggested queriesbeing generated based upon the plurality of relationship scores whichare generated based upon the plurality of search sessions and/or theplurality of sequences of queries. One or more suggested queries of thelist of suggested queries may be presented via the search interface.Alternatively and/or additionally, search results may be generated basedupon the first query and/or the list of suggested queries. The searchresults may be presented via the search interface.

An embodiment of generating a list of suggested queries associated withone or more keywords is illustrated by an example method 400 of FIG. 4.A user, such as user Jill, (and/or a first client device associated withthe user), may access and/or interact with a search interface configuredfor generating search results based upon queries. At 402, a graphicaluser interface of the first client device may be controlled to displaythe search interface. In some examples, the search interface may beassociated with a first web page accessed via a browser of the firstclient device. Alternatively and/or additionally, the search interfacemay be associated with a first application (e.g., a mobile application)accessed via the first client device.

In some examples, the search interface may be a web search enginedesigned to search for information throughout the Internet. For example,the search interface may be configured to generate search resultsassociated with database entries, websites and/or web pages.Alternatively and/or additionally, the search interface may be aninternal website search interface designed to search for informationcomprised within a defined set of one or more websites, a defined set ofone or more web pages and/or a defined set of one or more databases.

In some examples, the search interface may be configured for generatingsearch results associated with one or more types of content. Forexample, the one or more types of content may correspond to searchresults associated with product information and/or service information(e.g., the search results may be associated with pages associated withprices, vendors, etc. associated with the sale of products and/orservices). Alternatively and/or additionally, the one or more types ofcontent may correspond to search results associated with newsinformation (e.g., the search results may be associated with pagesassociated with news articles). Alternatively and/or additionally, theone or more types of content may correspond to search results associatedwith medical information (e.g., the search results may be associatedwith pages associated with medical information articles). Alternativelyand/or additionally, the one or more types of content may correspond tosearch results associated with legal information (e.g., the searchresults may be associated with pages associated with legal information).Alternatively and/or additionally, the one or more types of content maycorrespond to search results associated with job information (e.g., thesearch results may be associated with pages associated with jobpostings).

Alternatively and/or additionally, the search interface may beconfigured for generating search results associated with generalcontent. For example, the search interface may be configured forgenerating search results associated with one or more of productinformation, service information, news information, medical information,blog posts, web pages, etc. In some examples, the one or more types ofcontent associated with generating search results using the searchinterface may be selected and/or modified via a settings interface ofthe search interface.

At 404, one or more first keywords may be received via the searchinterface from the first client device. For example, the one or morefirst keywords may be entered (e.g., input) into a search field of thesearch interface. For example, the one or more first keywords maycomprise a set of characters (e.g., “smartphone”, “cars”, “J brandcars”, “j2 cars”, “night dress”, etc.). Alternatively and/oradditionally, the one or more first keywords may be received via thesearch interface and/or entered into the search field using atouchscreen (of the first client device), one or more switches (e.g.,one or more buttons, such as buttons of a keyboard), a conversationalinterface (e.g., a voice recognition and natural language interface),etc.

In some examples, the search interface may comprise a search selectableinput corresponding to performing a search based upon the one or morefirst keywords. For example, the one or more first keywords maycorrespond to a first query. A selection of the search selectable inputmay cause the search to be performed. In some examples, a firstplurality of search results associated with the one or more firstkeywords may be generated and/or presented via the search interface.

At 406, a first plurality of queries associated with the one or morefirst keywords may be determined based upon the one or more firstkeywords and/or a historical query database. In some examples, thehistorical query database may comprise a plurality of historical queriescomprising the first plurality of queries. In some examples, thehistorical query database may comprise timestamps associated withqueries of the plurality of historical queries (e.g., the timestamps maycorrespond to times that queries of the plurality of historical queriesare received and/or times that searches are performed using the queries)and/or indications of client devices associated with queries of theplurality of historical queries (e.g., the indications of client devicesmay correspond to client device identifiers associated with clientdevices from which queries of the plurality of historical queries arereceived).

In some examples, queries of the plurality of historical queries may beselected for inclusion in the first plurality of queries based upon adetermination that the queries are associated with the one or more firstkeywords. Alternatively and/or additionally, merely historical queriesof the historical query database received within a duration of timeprior to a current time may be analyzed to determine the first pluralityof queries (e.g., merely historical queries received within a year, amonth and/or a week prior to the current time may be analyzed todetermine the plurality of queries).

In some examples, an exemplary query of the plurality of historicalqueries may be selected for inclusion in the first plurality of queriesassociated with the one or more first keywords based upon adetermination that one or more characters and/or one or more keywords ofthe exemplary query match and/or are related to the one or more firstkeywords. For example, it may be determined that the one or morecharacters and/or the one or more keywords of the exemplary query matchand/or are related to the one or more first keywords if a portion of theexemplary query is the same as at least a portion of the one or morefirst keywords. In an example where the one or more first keywordscomprises “car” and/or the exemplary query comprises “offroad cars”, itmay be determined that the one or more characters and/or the one or morekeywords of the exemplary query match and/or are related to the one ormore first keywords based upon both the one or more first keywordsand/or the exemplary query comprising “car”.

Alternatively and/or additionally, it may be determined that the one ormore characters and/or the one or more keywords of the exemplary querymatch and/or are related to the one or more first keywords if at least aportion of the exemplary query is associated with a topic and/or asearch category associated with at least a portion of the one or morefirst keywords. In an example where the one or more first keywordscomprises “car” and/or the exemplary query comprises a car brand, it maybe determined that the one or more characters and/or the one or morekeywords of the exemplary query match and/or are related to the one ormore first keywords based upon the one or more first keywords and/or theexemplary query being associated with a topic and/or a search category(e.g., “cars”).

Alternatively and/or additionally, it may be determined that the one ormore characters and/or the one or more keywords of the exemplary querymatch and/or are related to the one or more first keywords if at least aportion of the exemplary query is associated with a definition and/or ameaning related to at least a portion of the one or more first keywords.In an example where the one or more first keywords comprises “phones”and/or the exemplary query comprises “smartphone”, it may be determinedthat the one or more characters and/or the one or more keywords of theexemplary query match and/or are related to the one or more firstkeywords based upon the one or more first keywords and/or the exemplaryquery being associated with similar definitions and/or meanings.

In some examples, the exemplary query may be associated with anexemplary search session. For example, the exemplary search session maycorrespond to searches performed via an exemplary client device usingthe exemplary query and/or one or more second exemplary queries. In someexamples, the historical query database may be indicative of theexemplary search session. Alternatively and/or additionally, timestampsand/or indications of client devices associated with historical queriesof the historical query database may be analyzed to determine theexemplary search session. The exemplary search session may correspond tosearches that are performed by the exemplary client device within aperiod of time. The searches associated with the exemplary searchsession may be performed using an exemplary search interface of theexemplary client device. Alternatively and/or additionally, the searchesassociated with the exemplary search session may be related to a singletopic. For example, the exemplary search session may correspond tosearches performed using the exemplary client device within a 30-minutetime period (and/or a different time period), wherein the searches maybe related to cars (and/or a different topic).

The one or more second exemplary queries may be selected for inclusionin the first plurality of queries associated with the one or more firstkeywords based upon a determination that the exemplary query and the oneor more second exemplary queries are associated with the first searchsession (e.g., searches performed using the exemplary query and the oneor more second exemplary queries may be performed in the first searchsession).

At 408, a plurality of relationship scores associated with the firstplurality of queries may be generated based upon a plurality of searchsessions associated with the plurality of historical queries. In someexamples, a relationship score of the plurality of relationship scoresmay be associated with a relationship between a query of the firstplurality of queries and the one or more first keywords. For example, arelationship score of the plurality of relationship scores may beassociated with a level of relevance of a query of the first pluralityof queries to the one or more first keywords. Alternatively and/oradditionally, a relationship score of the plurality of relationshipscores may be associated with a probability that the user associatedwith the first client device is interested in a query of the firstplurality of queries and/or a probability that the user will select thequery of the first plurality of queries.

In some examples, the plurality of search sessions may comprise a firstsearch session associated with a second client device. The first searchsession may correspond to one or more first searches performed via thesecond client device using one or more first queries of the plurality ofhistorical queries. For example, a second query of the one or more firstqueries associated with the first search session may be received fromthe second client device during the first search session at a first time(e.g., a first search of the one or more first searches may be performedresponsive to receiving the second query). Alternatively and/oradditionally, a third query of the one or more first queries may bereceived from the second client device during the first search sessionat a second time, after the first time (e.g., a second search of the oneor more first searches may be performed responsive to receiving thethird query).

In some examples, a first sequence of queries associated with the firstsearch session may be determined based upon the second query and/or thethird query (and/or one or more other queries of the one or more firstqueries). In some examples, the first sequence of queries may beindicative of one or more timing characteristics associated with the oneor more first searches. For example, the first sequence of queries maybe indicative of the third query being received after the second queryis received (and/or the first sequence of queries may be indicative ofthe second search being performed using the third query after the firstsearch is performed using the second query).

In some examples, the plurality of relationship scores may be generatedbased upon a plurality of sequences of queries associated with theplurality of search sessions. The plurality of sequences of queries maybe determined based upon the historical query database (e.g., thehistorical query database may be analyzed to determine the plurality ofsequences of queries). For example, the plurality of sequences ofqueries may comprise the first sequence of queries. Alternatively and/oradditionally, timestamps and/or indications of client devices associatedwith historical queries of the historical query database may be analyzedto determine the plurality of sequences of queries. In some examples,the historical query database may comprise and/or may be indicative ofthe plurality of sequences of queries. In some examples, the pluralityof sequences of queries may be indicative of queries received during theplurality of search sessions and/or search categories associated withthe queries. For example, the first sequence of queries may beindicative of the second query, a first search category associated withthe second query, the third query and/or a second search categoryassociated with the third query. In some examples, the plurality ofrelationship scores may be generated merely based upon search sessionsand/or sequences of queries that occur within the duration of time priorto the current time.

In an example, the second query may be associated with a name of a homeappliance brand (e.g., “JTX”) and/or the first search category may beassociated with home appliances (e.g., “home appliances”). Alternativelyand/or additionally, the third query may be associated with a phonemodel associated with the home appliance brand (e.g., “JTX 2 phone”)and/or the second search category may be associated with phones, whichmay be a sub-category of home appliances (e.g., “homeappliances-phones”). In some examples, the first sequence of queries maycomprise “JTX *Home appliances, JTX 2 phone *Home appliances-phones”).Alternatively and/or additionally, the first sequence of queries may beindicative of a last and/or most specific subcategory and/or category ofa query. For example, the first sequence of queries may comprise “JTX*Home appliances, JTX 2 phone *phones”).

In some examples, an exemplary relationship score associated with anexemplary query of the first plurality of queries may be generated basedupon a quantity of instances that a search associated with the exemplaryquery was performed (e.g., a quantity of search sessions where a queryassociated with and/or matching the exemplary query was received).Alternatively and/or additionally, the exemplary relationship scoreassociated with the exemplary query of the first plurality of queriesmay be generated based upon a quantity of sequences of queriescomprising a query associated with and/or matching the exemplary query.

Alternatively and/or additionally, a fourth query of the one or morefirst queries may be received from the second client device during thefirst search session at a third time, after the second time (e.g., athird search of the one or more first searches may be performedresponsive to receiving the fourth query). In some examples, the firstsequence of queries may be indicative of the fourth query being receivedafter the third query is received. Alternatively and/or additionally,the first sequence of queries may be indicative of one or more querysequence pairs. For example, a query sequence pair of the one or morequery sequence pairs may correspond to two queries of the first sequenceof queries.

For example, a query sequence pair of the one or more query sequencepairs may correspond to two consecutively received queries. A firstquery sequence pair of the one or more query sequence pairs maycorrespond to the second query and/or the third query. In some examples,the first query sequence pair may be indicative of the second query, thefirst search category associated with the second query, the third queryand/or the second search category associated with the third query.

Alternatively and/or additionally, a second query sequence pair of theone or more query sequence pairs may correspond to the third query andthe fourth query. In some examples, the second query and/or the fourthquery may not correspond to a query sequence pair as a result of thesecond query being received between reception of the second query andthe fourth query.

Alternatively and/or additionally, a query sequence pair of the one ormore query sequence pairs may correspond to two received queries of asequence of queries, regardless of whether a different query is receivedbetween reception of the two received queries. For example, a thirdquery sequence pair of the one or more query sequence pairs maycorrespond to the second query and the fourth query.

In some examples, the plurality of relationship scores may be generatedbased upon a plurality of query sequence pairs associated with theplurality of sequences of queries. For example, the plurality of querysequence pairs may comprise the query sequence pair, the second querysequence pair and/or the third query sequence pair. In some examples,the exemplary relationship score associated with the exemplary query ofthe first plurality of queries may be generated based upon a quantity ofquery sequence pairs comprising a query associated with and/or matchingthe one or more first keywords and/or comprising a query associated withand/or matching the exemplary query. For example, a query sequence pairthat comprises a query associated with and/or matching the one or morefirst keywords and comprises a query associated with and/or matching theexemplary query may be indicative of a search associated with theexemplary query being performed in a same search session that a searchassociated with the one or more first keywords is performed.Accordingly, it may be determined based upon the query sequence pairthat the exemplary query is related to and/or relevant to the one ormore first keywords.

Alternatively and/or additionally, positions of queries in a querysequence pair may be analyzed to generate the exemplary relationshipscore associated with the exemplary query. For example, a query sequencepair that comprises an initial query associated with and/or matching theone or more first keywords and comprises a next query associated withand/or matching the exemplary query may be indicative of a searchassociated with the exemplary query being performed after and/ordirectly after a search associated with the one or more first keywordsis performed (where the initial query is in a first position (e.g.,initial position) of the query sequence pair and the next query is in asecond position (e.g., after the initial position) of the query sequencepair which may be indicative of the initial query being received priorto the next query being received). Accordingly, it may be determinedbased upon the query sequence pair that the exemplary query is a followup query to the one or more first keywords. In some examples, theexemplary relationship score associated with the exemplary query may begenerated based upon a quantity of query sequence pairs indicative ofthe exemplary query (and/or a query associated with and/or matching theexemplary query) being a follow up query to the one or more firstkeywords (and/or a query associated with and/or matching the one or morefirst keywords).

In some examples, the exemplary relationship score of the exemplaryquery of the first plurality of queries may be indicative of a firstprobability that the user associated with the first client device(and/or a different user that performs a search associated with the oneor more first keywords) is interested in the exemplary query and/or asecond probability that the user will select the exemplary query if theexemplary query is presented (e.g., suggested to the user). In someexamples, the first probability and/or the second probability may bedetermined based upon one or more first query parameters associated withthe exemplary query. For example, the one or more first query parametersmay comprise the quantity of instances that a search associated with theexemplary query was performed, the quantity of sequences of queriescomprising a query associated with and/or matching the exemplary query,the quantity of query sequence pairs comprising a query associated withand/or matching the one or more first keywords and/or comprising a queryassociated with and/or matching the exemplary query and/or the quantityof query sequence pairs indicative of the exemplary query (and/or aquery associated with and/or matching the exemplary query) being afollow up query to the one or more first keywords (and/or a queryassociated with and/or matching the one or more first keywords).

Alternatively and/or additionally, the first probability and/or thesecond probability (and/or the first relationship score) may bedetermined based upon a comparison of the one or more first queryparameters with query parameters associated with other queries of thefirst plurality of queries. For example, one or more operations (e.g.,mathematical operations) may be performed using the one or more firstquery parameters and/or the query parameters associated with the otherqueries of the first plurality of queries to determine the firstprobability and/or the second probability (and/or the first relationshipscore).

In some examples, the plurality of relationship scores may be comparedwith a threshold relationship score. For example, responsive to adetermination that the exemplary relationship score does not meet thethreshold relationship score, the exemplary query may be removed from(and/or may not be included in) the first plurality of queries.

In some examples, the plurality of relationship scores may be generatedusing a machine learning model. The machine learning model may have anencoder decoder architecture (e.g., a sequence-to-sequence (Seq2Seq)architecture) and/or a different machine learning architecture. In someexamples, the machine learning model may be trained using the pluralityof sequences of queries associated with the plurality of searchsessions. A first plurality of representations (e.g., one or more ofvector representations, embeddings, word embeddings, etc.) may begenerated based upon the plurality of sequences of queries (and/or asecond plurality of sequences of queries of the historical querydatabase associated with search sessions different than the plurality ofsearch sessions). For example, a representation of the first pluralityof representations may be generated based upon a sequence of queries ofthe plurality of sequences of queries. In some examples, the firstplurality of representations may correspond to numerical representationsof the plurality of sequences of queries (e.g., a representation of thefirst plurality of representations may be a numerical representationand/or a numerical embedding of a sequence of queries of the pluralityof sequences of queries).

In an example, a first exemplary representation of the first pluralityof representations may be generated based upon an exemplary sequence ofqueries of the plurality of sequences of queries. In some examples, thefirst exemplary representation may be indicative of exemplary queriesassociated with the exemplary sequence of queries. Alternatively and/oradditionally, the first exemplary representation may be indicative ofone or more timing characteristics and/or a temporal sequence of queriesassociated with the exemplary sequence of queries. Alternatively and/oradditionally, the first exemplary representation may be indicative ofone or more search categories associated with the exemplary queriesassociated with the exemplary sequence of queries.

In some examples, the first plurality of representations may begenerated using one or more word2vec techniques. For example, arepresentation of the first plurality of representations (and/or eachrepresentation of the first plurality of representations) may begenerated using the one or more word2vec techniques. Alternativelyand/or additionally, a representation of the first plurality ofrepresentations (and/or each representation of the first plurality ofrepresentations) may correspond to a word2vec embedding (e.g., anumerical representation) associated with a sequence of queries of theplurality of sequences of queries. It may be appreciated that word2vecis an exemplary algorithm configured to receive as input a corpus oftext and generate vectors and/or numerical representations. Whileword2vec may be mentioned herein, one or more other algorithms similarlyconfigured to receive as input a corpus of text and generate vectorsand/or numerical representations may be used instead (and/or inaddition), and are contemplated.

Alternatively and/or additionally, the machine learning model may betrained using the plurality of query sequence pairs associated with theplurality of sequences of queries. A second plurality of representations(e.g., one or more of vector representations, embeddings, wordembeddings, etc.) may be generated based upon the plurality of querysequence pairs (and/or a second plurality of query sequence pairs of thehistorical query database associated with search sessions different thanthe plurality of search sessions). For example, a representation of thesecond plurality of representations may be generated based upon a querysequence pair of the plurality of query sequence pairs. In someexamples, the second plurality of representations may correspond tonumerical representations of the plurality of query sequence pairs(e.g., a representation of the second plurality of representations maybe a numerical representation and/or a numerical embedding of a querysequence pair of the plurality of query sequence pairs).

In an example, a second exemplary representation of the second pluralityof representations may be generated based upon an exemplary querysequence pair of the plurality of query sequence pairs. In someexamples, the second exemplary representation may be indicative ofexemplary queries associated with the exemplary query sequence pair.Alternatively and/or additionally, the second exemplary representationmay be indicative of one or more timing characteristics associated withthe exemplary query sequence pair. Alternatively and/or additionally,the second exemplary representation may be indicative of one or moresearch categories associated with the exemplary queries associated withthe exemplary query sequence pairs.

In some examples, the second plurality of representations may begenerated using the one or more word2vec techniques. For example, arepresentation of the second plurality of representations (and/or eachrepresentation of the second plurality of representations) may begenerated using the one or more word2vec techniques. Alternativelyand/or additionally, a representation of the second plurality ofrepresentations (and/or each representation of the second plurality ofrepresentations) may correspond to a word2vec embedding (e.g., anumerical representation) associated with a query sequence pair of theplurality of query sequence pairs.

In some examples, the machine learning model may be trained using thefirst plurality of representations (associated with the plurality ofsequences of queries) and/or the second plurality of representations(associated with the plurality of query sequence pairs). Alternativelyand/or additionally, the first plurality of representations and/or thesecond plurality of representations may be input to the machine learningmodel to determine the first plurality of queries and/or generate theplurality of relationship scores using the first plurality ofrepresentations and/or the second plurality of representations.

In some examples, the machine learning model may use one or morerecurrent neural networks (RNNs) to process the one or more firstkeywords and/or one or more queries received via the search interfacefrom the first client device during a search session within which theone or more first keywords are received. Alternatively and/oradditionally, the machine learning model may determine the firstplurality of queries and/or generate the plurality of relationshipscores based upon the historical query database, the first plurality ofrepresentations (associated with the plurality of sequences of queries)and/or the second plurality of representations (associated with theplurality of query sequence pairs).

In some examples, the machine learning model may convert an inputsequence into an output sequence. Alternatively and/or additionally, theinput sequence and/or the output sequence may have arbitrary lengths.For example, a character length and/or a sequence length of the inputsequence may be different than a character length and/or a sequencelength of the output sequence. In some examples, the input sequence maycorrespond to the one or more first keywords and/or the one or morequeries received via the search interface from the first client device.

Alternatively and/or additionally, the machine learning model may beassociated with one or more long short-term memory (LSTM) layers and/orone or more states associated with the one or more LSTM layers. In someexamples, one or more representations (e.g., one or more of one or morevector representations, one or more numerical representations, one ormore embeddings, one or more word embeddings, etc.) may be generated bya state (e.g., a last state) of the one or more states associated withthe one or more LSTM layers, based upon the input sequence.Alternatively and/or additionally, the machine learning model may beassociated with one or more encoder LSTM states associated with LSTMstates of an encoder of the machine learning model and/or one or moredecoder LSTM states associated with LSTM states of a decoder of themachine learning model. For example, the decoder may initialize the oneor more decoder LSTM states based upon the one or more encoder LSTMstates.

In some examples, the one or more representations may be compared withthe first plurality of representations (associated with the plurality ofsequences of queries) and/or the second plurality of representations(associated with the plurality of query sequence pairs) to determinerelationships between the one or more representations andrepresentations of the first plurality of representations and/or thesecond plurality of representations. Alternatively and/or additionally,the one or more representations may be compared with the first pluralityof representations and/or the second plurality of representations togenerate the plurality of relationship scores.

In some examples, the plurality of relationship scores may be normalizedinto a probability distribution associated with a plurality ofprobabilities associated with the first plurality of queries. Forexample, a probability of the plurality of probabilities associated witha query of the first plurality of queries may be indicative of aprobability that the user associated with the first client device isinterested in the query. Alternatively and/or additionally, aprobability of the plurality of probabilities associated with a query ofthe first plurality of queries may be indicative of a probability thatthe user associated with the first client device will select the queryif the query is presented (e.g., suggested to the user). For example,the plurality of probabilities may add up to 1 (and/or 100%).Alternatively and/or additionally, the plurality of probabilities may becompared with a threshold probability. For example, responsive to adetermination that a probability of the plurality of probabilities doesnot meet the threshold probability, a query associated with theprobability may be removed from (and/or may not be included in) thefirst plurality of queries. In some examples, the plurality ofrelationship scores may be normalized into the probability distributionassociated with the plurality of probabilities using a softmax function(e.g., a normalized exponential function).

In some examples, the first plurality of queries may comprise a firstplurality of sets of queries. A first set of queries of the firstplurality of sets of queries may be determined based upon the one ormore first keywords and/or the one or more queries received via thesearch interface from the first client device (e.g., the first set ofqueries may be determined using the machine learning model). Forexample, the first set of queries may be associated with and/or mayyield search results providing information associated with the one ormore first keywords (e.g., if the one or more first keywords comprises“cars”, the first set of queries may be associated with one or more carbrands). In some examples, the first set of queries may be associatedwith a first set of probabilities (of the plurality of probabilities)that meet the probability threshold (e.g., queries that are associatedwith probabilities that do not meet the probability threshold may beremoved from the first set of queries and/or may not be included in thefirst set of queries). Alternatively and/or additionally, the first setof queries may be associated with a first set of quantities (e.g.,quantities of searches performed using queries of the first set ofqueries) that meet a quantity threshold (e.g., each query of the firstset of queries may be associated with a quantity of searches performedwithin the duration of time prior to the current time using the querythat meets the quantity threshold).

In some examples, one or more first sets of queries of the firstplurality of sets of queries may be determined based upon the first setof queries. Alternatively and/or additionally, for each query of thefirst set of queries, a set of queries may be determined. For example, asecond set of queries of the one or more first sets of queries may bedetermined based upon the one or more first keywords, the one or morequeries received via the search interface from the first client deviceand/or a fifth query of the first set of queries. For example, thesecond set of queries may be associated with and/or may yield searchresults providing information associated with the fifth query (e.g., ifthe fifth query comprises a first car brand, the second set of queriesmay be associated with one or more car models of the first car brand).In some examples, the second set of queries may be associated with asecond set of probabilities (of the plurality of probabilities) thatmeet the probability threshold (e.g., queries that are associated withprobabilities that do not meet the probability threshold may be removedfrom the second set of queries and/or may not be included in the secondset of queries). Alternatively and/or additionally, the second set ofqueries may be associated with a second set of quantities (e.g.,quantities of searches performed using queries of the second set ofqueries) that meet the quantity threshold (e.g., each query of thesecond set of queries may be associated with a quantity of searchesperformed within the duration of time prior to the current time usingthe query that meets the quantity threshold).

Alternatively and/or additionally, a third set of queries of the one ormore first sets of queries may be determined (by the machine learningmodel) based upon the one or more first keywords, the one or morequeries received via the search interface from the first client deviceand/or a sixth query of the first set of queries. For example, the thirdset of queries may be associated with and/or may yield search resultsproviding information associated with the sixth query (e.g., if thesixth query comprises a second car brand, the third set of queries maybe associated with one or more car models of the second car brand). Insome examples, the third set of queries may be associated with a thirdset of probabilities (of the plurality of probabilities) that meet theprobability threshold (e.g., queries that are associated withprobabilities that do not meet the probability threshold may be removedfrom the third set of queries and/or may not be included in the thirdset of queries). Alternatively and/or additionally, the third set ofqueries may be associated with a third set of quantities (e.g.,quantities of searches performed using queries of the second set ofqueries) that meet the quantity threshold (e.g., each query of the thirdset of queries may be associated with a quantity of searches performedwithin the duration of time prior to the current time using the querythat meets the quantity threshold).

Alternatively and/or additionally, one or more second sets of queriesmay be determined based upon the second set of queries. For example, afourth set of queries of the one or more second sets of queries may bedetermined based upon a sixth query of the second set of queries.Alternatively and/or additionally, one or more third sets of queries maybe determined based upon the third set of queries. For example, a fifthset of queries of the one or more third sets of queries may bedetermined based upon a seventh query of the third set of queries.

In some examples, a first query path may be determined based upon thefirst plurality of sets of queries. For example, the first query pathmay correspond to the fifth query of the first set of queries, thesecond set of queries associated with the fifth query and/or the one ormore second sets of queries associated with the second set of queries.For example, the fifth query may correspond to a first step of the firstquery path, the second set of queries may correspond to a second step ofthe first query path, the one or more second sets of queries maycorrespond to a third step of the first query path, etc.

Alternatively and/or additionally, a second query path may be determinedbased upon the first plurality of sets of queries. For example, thesecond query path may correspond to the sixth query of the first set ofqueries, the third set of queries associated with the sixth query and/orthe one or more third sets of queries associated with the third set ofqueries. For example, the sixth query may correspond to a first step ofthe second query path, the third set of queries may correspond to asecond step of the second query path, the one or more third sets ofqueries may correspond to a third step of the second query path, etc. Insome examples, the first query path and/or the second query path may bedetermined using the machine learning model. Alternatively and/oradditionally, one or more other query paths may be determined based uponone or more other queries of the first set of queries.

At 410, the historical query database may be analyzed to determine aplurality of click rates (e.g., click-through rates (CTRs)) associatedwith the first plurality of queries. An exemplary click rate, of theplurality of click rates, associated with an exemplary query maycorrespond to a proportion (e.g., a percentage) of searches associatedwith the exemplary query leading to a selection of a search result(and/or a link associated with the search result) generated based uponthe exemplary query. For example, the exemplary click rate may bedetermined based upon a total quantity of searches of a plurality ofsearches associated with the exemplary query and/or a quantity ofsearches of a second plurality of searches (of the plurality ofsearches) associated with a selection of a search result generated basedupon the exemplary query. For example, one or more operations (e.g.,mathematical operations) may be performed using the total quantity ofsearches and/or the quantity of searches to determine the exemplaryclick rate.

In some examples, the plurality of relationship scores may be generatedbased upon the plurality of click rates. For example, a first pluralityof weights may be generated based upon the plurality of click rates. Forexample, the first plurality of weights may be assigned to the firstplurality of queries. Alternatively and/or additionally, the pluralityof relationship scores may be generated based upon the first pluralityof weights. In some examples, a query with a higher weight of the firstplurality of weights may be assigned a higher relationship score ascompared with a query with a lower weight of the first plurality ofweights. In some examples, the plurality of click rates and/or the firstplurality of weights may be input to the machine learning model forgenerating the plurality of relationship scores. Alternatively and/oradditionally, the machine learning model may be trained using theplurality of click rates and/or the first plurality of weights.

Alternatively and/or additionally, the historical query database may beanalyzed to determine a second plurality of click rates associated withthe plurality of query sequence pairs associated with the plurality ofsequences of queries. For example, a click rate associated with anexemplary query sequence pair of the plurality of query sequence pairsmay be determined. In some examples, a second plurality of weights maybe generated based upon the second plurality of click rates. Forexample, the second plurality of weights may be assigned to theplurality of query sequence pairs. In some examples, the plurality ofrelationship scores may be generated based upon the second plurality ofclick rates and/or the second plurality of weights. In some examples, aquery associated with a query sequence pair with a higher weight of thesecond plurality of weights may be assigned a higher relationship scoreas compared with a query associated with a query sequence pair with alower weight of the second plurality of weights. In some examples, thesecond plurality of click rates and/or the second plurality of weightsmay be input to the machine learning model for generating the pluralityof relationship scores. Alternatively and/or additionally, the machinelearning model may be trained using the second plurality of click ratesand/or the second plurality of weights.

In an example, the second query of the first query sequence pair(corresponding to the second query and the third query) may beassociated with two selections, performed via the second client device,of search results generated based upon the second query and/or the thirdquery of the first query sequence pair may be associated with oneselection of a search result generated based upon the third query.Alternatively and/or additionally, the fourth query of the secondsequence pair (corresponding to the third query and the fourth query)may be associated with four selections of search results generated basedupon the fourth query. Accordingly, a first quantity of selectionsassociated with the first query sequence pair may be three selectionsand/or a second quantity of selections associated with the second querysequence pair may be five selections. Thus, a first click rate and/or afirst weight associated with the first query sequence pair may be lessthan a second click rate and/or a second weight associated with thesecond query sequence pair.

In some examples, a plurality of search frequencies associated with thefirst plurality of queries may be determined. In some examples, a searchfrequency of the plurality of search frequencies may correspond to arate per duration of time at which a query of the first plurality ofqueries is used to perform a search (e.g., the duration of time maycorrespond to one or more of an hour, a day, a week, a month, etc.and/or a different duration of time). In some examples, a plurality ofsearch frequency weights may be generated based upon the plurality ofsearch frequencies. For example, the plurality of search frequencyweights may be assigned to the first plurality of queries. In someexamples, the plurality of relationship scores may be generated basedupon the plurality of search frequencies and/or the plurality of searchfrequency weights. In some examples, a query with a higher searchfrequency weight (and/or a higher search frequency) may be assigned ahigher relationship score as compared with a query with a lower searchfrequency weight (and/or a lower search frequency).

In some examples, queries associated with search frequencies lower thana threshold search frequency may not be included in the first pluralityof queries and/or may be removed from the first plurality of queries.Alternatively and/or additionally, the plurality of sequences of queriesmay be modified based upon the plurality of search frequencies. Forexample, queries associated with search frequencies lower than thethreshold search frequency may not be included in and/or may be removedfrom the plurality of sequences of queries. Alternatively and/oradditionally, query pairs of the plurality of query sequence pairs maybe determined based upon a version of the plurality of sequences ofqueries without the queries associated with the search frequencies lowerthan the threshold search frequency. Thus, the plurality of querysequence pairs may not comprise (and/or may comprise) the queriesassociated with the search frequencies lower than the threshold searchfrequency.

At 412, a first list of suggested queries associated with the one ormore first keywords may be generated based upon the first plurality ofqueries, the plurality of relationship scores, the plurality of clickrates, the second plurality of click rates, the plurality of searchfrequencies, the plurality of sequences of queries and/or the pluralityof query sequence pairs. In some examples, the first list of suggestedqueries may be generated and/or output by the machine learning model.

For example, the first plurality of queries may be ranked based upon thefirst plurality of queries, the plurality of relationship scores, theplurality of click rates, the second plurality of click rates, theplurality of search frequencies, the plurality of sequences of queriesand/or the plurality of query sequence pairs to generate a plurality ofrankings associated with the first plurality of queries. In someexamples, the first list of suggested queries may be generated basedupon the plurality of rankings. For example, the first list of suggestedqueries may be arranged (e.g., organized) based upon the plurality ofrankings. For example, a first exemplary suggested query of the firstlist of suggested queries may be above a second exemplary query of thefirst list of suggested queries based upon a first exemplary ranking ofthe first exemplary query being higher than a second exemplary rankingof the second exemplary query.

It may be appreciated that by ranking the first plurality of queriesbased upon the first plurality of queries, the plurality of relationshipscores, the plurality of click rates, the second plurality of clickrates, the plurality of search frequencies, the plurality of sequencesof queries and/or the plurality of query sequence pairs to generate theplurality of rankings, informative queries may be ranked higher thanother queries of the first plurality of queries. In some examples, theinformative queries may correspond to queries that may yield searchresults that may provide the user associated with the first clientdevice with background information associated with a subject associatedwith the one or more first keywords.

In some examples, the one or more first keywords may be modified basedupon the first list of suggested queries. For example, the one or morefirst keywords may be compared with the first list of suggested queriesto determine whether the one or more first keywords comprise one or moreof a misspelling, a misused word, etc. For example, responsive to adetermination that the one or more first keywords comprises amisspelling and/or a misused word, the one or more first keywords may bemodified to a modified version of the one or more first keywordsautomatically and/or the first plurality of search results may begenerated based upon the modified version of the one or more firstkeywords (rather than the one or more first keywords). For example, themodified version of the one or more first keywords may be determinedbased upon a suggested query of the first list of suggested queries thatis similar in meaning and/or spelling to the one or more first keywords.Alternatively and/or additionally, responsive to a determination thatthe one or more first keywords comprises a misspelling and/or a misusedword, the modified version of the one or more first keywords may bepresented via the search interface. For example, responsive to aselection of the modified version of the one or more first keywords, thefirst plurality of search results may be generated based upon themodified version of the one or more first keywords.

In some examples, one or more first suggested queries of the first listof suggested queries may be presented via the search interface. In someexamples, responsive to receiving a selection of a suggested query ofthe one or more first suggested queries via the search interface, asearch associated with the suggested query may be performed to generateand/or present a second plurality of search results associated with thesuggested query.

For example, the one or more first suggested queries of the first listof suggested queries may be presented via the search interface basedupon the plurality of rankings. For example, the one or more firstsuggested queries of the first list of suggested queries may be selectedfor presentation via the search interface based upon a determinationthat the one or more first suggested queries are associated with one ormore first rankings that meet (and/or are above) a rank threshold. Forexample, the one or more first rankings may meet the rank thresholdbased upon the one or more first rankings being above the rankthreshold. Alternatively and/or additionally, the one or more firstrankings may meet the rank threshold based upon the one or more firstrankings being higher than other rankings of the plurality of rankings.For example, the one or more first suggested queries of the first listof suggested queries may be selected for presentation via the searchinterface responsive to a determination that the one or more firstsuggested queries are associated with the highest rankings of theplurality of rankings. For example, if the rank threshold is associatedwith five highest ranked queries, the one or more first suggestedqueries may comprise five suggested queries of the first list ofsuggested queries associated with rankings that are higher than otherrankings of the plurality of rankings.

In some examples, the one or more first suggested queries of the firstlist of suggested queries may be presented adjacent to the search field(and/or at a different location) of the search interface. Alternativelyand/or additionally, the one or more first suggested queries of thefirst list of suggested queries may be may be presented while the one ormore first keywords are input into the search field of the searchinterface and/or after the one or more first keywords are input into thesearch field. For example, a recommended search list (e.g., such as aquery prediction list and/or an auto-complete list) comprising the oneor more first suggested queries may be presented adjacent to the searchfield (and/or at a different location of the search interface).

Alternatively and/or additionally, the one or more first suggestedqueries of the first list of suggested queries may be presentedresponsive to the first plurality of search results associated with theone or more first keywords being generated (based upon the one or morefirst keywords and/or the first query comprising the one or more firstkeywords) and/or presented via the search interface. For example, theone or more first suggested queries of the first list of suggestedqueries may be presented responsive to receiving the selection of thesearch selectable input corresponding to performing a search based uponthe one or more first keywords. For example, the one or more firstsuggested queries of the first list of suggested queries may bepresented adjacent to the search field and/or adjacent to the firstplurality of search results associated with the one or more firstkeywords (and/or at a different location of the search interface).

Alternatively and/or additionally, after the first plurality of searchresults are generated and/or presented via the search interface, the oneor more first suggested queries of the first list of suggested queriesmay be presented. For example, responsive to receiving the selection ofthe search selectable input, the search interface may present the searchfield and/or the first plurality of search results. In some examples,the one or more first suggested queries may be presented responsive toreceiving a selection of the search field of the search interface. Forexample, responsive to receiving the selection of the search field ofthe search interface, the recommended search list comprising the one ormore first suggested queries may be presented adjacent to the searchfield (and/or at a different location of the search interface).Alternatively and/or additionally, responsive to detecting one or morecharacters being input into the search field, the recommended searchlist comprising the one or more first suggested queries may be presentedadjacent to the search field (and/or at a different location of thesearch interface).

Alternatively and/or additionally, the first plurality of search resultsmay be generated based upon the one or more first keywords, the firstlist of suggested queries and/or the one or more first suggestedqueries. For example, the first plurality of search results may bearranged (e.g., organized) based upon the first list of suggestedqueries and/or the one or more first suggested queries. For example, asearch result associated with a query of the one or more first suggestedqueries may be presented above (e.g., preceding and/or higher up in thefirst plurality of search results) a search result that is notassociated with the one or more first suggested queries.

Alternatively and/or additionally, a first set of search results may begenerated based upon the one or more first keywords and/or a second setof search results may be generated based upon the one or more firstsuggested queries (and/or one or more queries of the first list ofsuggested queries). For example, the first plurality of search resultsmay be generated based upon the first set of search results and/or thesecond set of search results. For example, the first plurality of searchresults may comprise the first set of search results and/or the secondset of search results. It may be appreciated that by generating and/orarranging the first plurality of search results based upon the one ormore first suggested queries, the first plurality of search results maycomprise more informative search results and/or the informative searchresults may be more easily accessible to the user (e.g., the informativesearch results may be presented higher up in the first plurality ofsearch results such that the user may not be required to extensivelybrowse and/or navigate through search results to find an informativesearch result). For example, the informative search results maycorrespond to search results that provide the user associated with thefirst client device with background information associated with asubject associated with the one or more first keywords).

Alternatively and/or additionally, the first plurality of search resultsmay be generated based upon the one or more first keywords. The firstplurality of search results may be ranked based upon the first list ofsuggested queries, the plurality of rankings associated with the firstlist of suggested queries and/or the one or more first suggested queriesto generate a second plurality of rankings associated with the firstplurality of search results. In some examples, a ranking of the secondplurality of rankings may be generated based upon a level of relevanceof a search result of the first plurality of search results with thefirst list of suggested queries and/or the one or more first suggestedqueries. In some examples, a ranked list of search results may begenerated based upon the first plurality of search results and/or thesecond plurality of rankings. For example, the first plurality of searchresults may be arranged (based upon the second plurality of rankings) togenerate the ranked list of search results. For example, a search resultof the first plurality of search results with a higher ranking of thesecond plurality of rankings may be presented above (e.g., precedingand/or higher up in the ranked list of search results) a search resultthat with a lower ranking of the second plurality of rankings. It may beappreciated that by ranking the first plurality of search results basedupon the first list of suggested queries, the plurality of rankingsassociated with the first list of suggested queries and/or the one ormore first suggested queries to generate the ranked list of searchresults, informative search results may be more easily accessible to theuser (e.g., the informative search results may be presented higher up inthe ranked list of search results such that the user may not be requiredto extensively browse and/or navigate through search results to find aninformative search result).

In some examples, the first list of suggested queries may be generatedbased upon the first query path and/or the second query path. Forexample, one or more first step queries of the first set of queries maybe included in the first list of suggested queries. For example, the oneor more first step queries may correspond to the fifth query associatedwith the first step of the first query path and/or the sixth queryassociated with the first step of the second query path. Alternativelyand/or additionally, the one or more first suggested queries of thefirst list of suggested queries selected for presentation via the searchinterface may comprise the one or more first step queries.

In some examples, a selection of a query of the one or more first stepqueries may be received via the search interface. For example, aselection of the fifth query associated with the first step of the firstquery path may be received. In some examples, responsive to receivingthe selection of the fifth query associated with the first step of thefirst query path, a search associated with the fifth query may beperformed to generate and/or present a third plurality of search resultsassociated with the fifth query. Alternatively and/or additionally, asecond list of suggested queries may be generated and/or presented viathe search interface responsive to receiving the selection of the fifthquery associated with the first step of the first query path. Forexample, the second list of suggested queries may comprise one or morequeries associated with the second step of the second query path. Forexample, one or more second step queries associated with the first querypath (e.g., one or more queries of the second set of queries associatedwith the second step of the first query path) may be included in thesecond list of suggested queries and/or may be presented via the searchinterface.

FIGS. 5A-5F illustrate examples of a system 501 for generating a list ofsuggested queries associated with one or more keywords. FIG. 5Aillustrates a first device 500 (e.g., the first client device)presenting and/or accessing the search interface. The search interfacemay be accessed via a browser of the first device 500. For example, thebrowser may comprise an address bar 502 comprising a web address (e.g.,a Uniform Resource Locator (URL)) of a first web page 508 associatedwith the search interface. In some examples, the first web page 508 maycomprise a search field 506. For example, the one or more first keywords(e.g., “Smartphone”) may be entered into the search field 506. In someexamples, the first web page 508 may comprise a search selectable input504 corresponding to performing a search based upon the one or morefirst keywords. For example, the search selectable input 504 may beselected. Alternatively and/or additionally, the first plurality ofqueries may be determined based upon the one or more first keywords (bythe machine learning model). Alternatively and/or additionally, thefirst plurality of sets of queries (of the first plurality of queries)may be determined based upon the one or more first keywords.

FIG. 5B illustrates a plurality of query paths associated with the oneor more first keywords being determined. For example, a first query path510 (e.g., the first query path) and/or a second query path 512 (e.g.,the second query path) may be determined based upon the one or morefirst keywords. For example, a first set of queries of the firstplurality of sets of queries may be determined based upon the one ormore first keywords. In some examples, the first set of queries may beassociated with and/or may yield search results providing informationassociated with smartphones (e.g., the first set of queries may comprisesmartphone brands).

The first set of queries may comprise a first query 516 (e.g., “GRX”)associated with a first smartphone brand. In some examples, the firstquery 516 may be associated with a first step of the first query path510. Alternatively and/or additionally, the first set of queries maycomprise a second query 518 (e.g., “TXL”) associated with a secondsmartphone brand. In some examples, the second query 518 may beassociated with a first step of the second query path 512.

In some examples, a second set of queries of the first plurality of setsof queries may be determined based upon the first query 516. Forexample, the second set of queries may comprise a third query 520 (e.g.,“GRX 10”) associated with a first smartphone model associated with thefirst smartphone brand and/or a fourth query 522 (e.g., “GRX 11”)associated with a second smartphone model associated with the firstsmartphone brand. In some examples, the second set of queries may beassociated with a second step of the first query path 510.

Alternatively and/or additionally, a third set of queries of the firstplurality of sets of queries may be determined based upon the secondquery 518. For example, the third set of queries may comprise a fifthquery 524 (e.g., “TXL T2”) associated with a third smartphone modelassociated with the second smartphone brand and/or a sixth query 526(e.g., “TXL Z3”) associated with a fourth smartphone model associatedwith the second smartphone brand. In some examples, the third set ofqueries may be associated with a second step of the second query path512.

Alternatively and/or additionally, one or more first sets of queries ofthe first plurality of sets of queries may be determined based upon thesecond set of queries. In some examples, the one or more first sets ofqueries may be associated with a third step of the first query path 510.For example, a fourth set of queries of the one or more first sets ofqueries may be determined based upon the third query 520. The fourth setof queries may comprise a seventh query 528 (e.g., “GRX 10 Phone”)associated with a product associated with the first smartphone modeland/or an eighth query 530 (e.g., “GRX 10 Case”) associated with aproduct associated with the first smartphone model. Alternatively and/oradditionally, a fifth set of queries of the one or more first sets ofqueries may be determined based upon the fourth query 522. The fifth setof queries may comprise a ninth query 532 (e.g., “GRX 11 Phone”)associated with a product associated with the second smartphone modeland/or a tenth query 534 (e.g., “GRX 11 Case”) associated with a productassociated with the second smartphone model.

Alternatively and/or additionally, one or more second sets of queries ofthe first plurality of sets of queries may be determined based upon thethird set of queries. In some examples, the one or more second sets ofqueries may be associated with a third step of the second query path512. For example, a sixth set of queries of the one or more second setsof queries may be determined based upon the fifth query 524. The sixthset of queries may comprise an eleventh query 536 (e.g., “TXL T2 Phone”)associated with a product associated with the third smartphone modeland/or a twelfth query 538 (e.g., “TXL T2 Case”) associated with aproduct associated with the third smartphone model. Alternatively and/oradditionally, a seventh set of queries of the one or more second sets ofqueries may be determined based upon the sixth query 526. The seventhset of queries may comprise a thirteenth query 540 (e.g., “TXL Z3Phone”) associated with a product associated with the fourth smartphonemodel and/or a fourteenth query 542 (e.g., “TXL Z3 Case”) associatedwith a product associated with the fourth smartphone model.

FIG. 5C illustrates the first device 500 presenting a first list ofsearch results associated with the one or more first keywords and/or afirst list of suggested queries associated with the one or more firstkeywords. In some examples, the first list of suggested queries may bedetermined based upon the one or more first keywords and/or theplurality of sets of keywords. In some examples, one or more firstsuggested queries of the first list of suggested queries may bepresented via the search interface. For example, the one or more firstsuggested queries may comprise one or more queries of the first set ofqueries. For example, the one or more first suggested queries maycomprise the first query 516 associated with the first step of the firstquery path 510, the second query 518 associated with the first step ofthe second query path 512 and/or a fifteenth query 554 (e.g., “ZOLED”)associated with a third smartphone brand.

Alternatively and/or additionally, the first list of search results maybe generated based upon the one or more first keywords and/or the one ormore first suggested queries. For example, a first search result 556and/or a second search result 562 may correspond to web pages associatedwith the one or more first keywords. Alternatively and/or additionally,a third search result 558 may correspond to a web page associated withthe first query 516 (e.g., the third search result 558 may be determinedbased upon the first query 516). Alternatively and/or additionally, afourth search result 560 may correspond to a web page associated withthe second query 518 (e.g., the fourth search result 560 may bedetermined based upon the second query 518). In some examples, aselection of the first query 516 may be received via the first list ofsuggested queries.

FIG. 5D illustrates the first device 500 presenting a second list ofsearch results associated with the first query 516 and/or a second listof suggested queries associated with the first query 516. For example,the second list of suggested queries may be determined based upon thefirst query 516. For example, the second list of suggested queries maybe determined and/or the second list of search results may be presentedresponsive to receiving the selection of the first query 516. Forexample, the second list of suggested queries may comprise the thirdquery 520, the fourth query 522 and/or a sixteenth query 572 (e.g., “GRX9”) associated with the second step of the first query path 510. Forexample, the second list of suggested queries may comprise queriesassociated with the second step of the first query path 510 based uponthe selection of the first query 516 associated with the first step ofthe first query path 510. In some examples, a selection of the thirdquery 520 may be received via the second list of suggested queries.

FIG. 5E illustrates the first device 500 presenting a third list ofsearch results associated with the third query 520 and/or a third listof suggested queries associated with the third query 520. For example,the third list of suggested queries may be determined based upon thethird query 520. For example, the third list of suggested queries may bedetermined and/or the third list of search results may be presentedresponsive to receiving the selection of the third query 520. Forexample, the third list of suggested queries may comprise the seventhquery 528 and/or the eighth query 530 associated with the third step ofthe first query path 510 (and/or associated with the third query 520).For example, the third list of suggested queries may comprise queriesassociated with the third step of the first query path 510 (and/orassociated with the third query 520) based upon the selection of thethird query 520 associated with the second step of the first query path510.

In some examples, a fifth search result 588 of the third list of searchresults may be associated with a second web page 548 (illustrated inFIG. 5F). For example, a selection of the fifth search result 588 may bereceived.

FIG. 5F illustrates the first device 500 presenting the second web page548. In some examples, the second web page 548 may be presentedresponsive to the selection of the fifth search result 588.

It may be appreciated that the disclosed subject matter may assist auser (and/or a client device associated with the user) in identifyingqueries associated with one or more keywords entered into a searchinterface and/or in using the queries to obtain informative searchresults providing the user with information associated with the one ormore keywords.

Implementation of at least some of the disclosed subject matter may leadto benefits including, but not limited to, a technical improvement tothe functionality of a computer-implemented search engine and/or acomputer-implemented content provider, and/or a reduction in screenspace and/or an improved usability of a display (e.g., of the clientdevice) (e.g., as a result of determining suggested queries based uponsequences of queries in a historical query database and/or based uponquery sequence pairs in the historical query database, as a result ofranking the suggested queries and/or presenting one or more suggestedqueries having rankings above a threshold rank such that the user maymore quickly and/or easily find queries that the user has an interest inand/or such that the user may more quickly and/or easily find one ormore search results that the user has an interest in, wherein the userdoes not need to navigate through various search results to find the oneor more search results that the user has an interest in, etc.).

Alternatively and/or additionally, implementation of at least some ofthe disclosed subject matter may lead to benefits including an improvedefficiency and/or speed of a computer-implemented search engine (e.g.,as a result of determining suggested queries associated with a pluralityof query paths and/or multiple steps of each query path based upon oneor more received keywords such that one or more second suggested queriesassociated with a second step of a first query path may be determinedprior to receiving a selection of a suggested query associated with afirst step of the first query path, wherein responsive to receiving theselection of the suggested query associated with the first step of thefirst query path, the one or more second suggested queries associatedwith the second step of the first query path may be more quicklypresented as a result of determining the one or more second suggestedqueries associated with the second step prior to receiving the selectionof the suggested query associated with the first step and/or storing theone or more second suggested queries in memory (e.g., a cache), etc.).

Alternatively and/or additionally, implementation of at least some ofthe disclosed subject matter may lead to benefits including a reductionin bandwidth (e.g., as a result of providing improved and/or moreinformative query suggestions such that a need to perform multiplesearches is decreased due to the improved query suggestions, as a resultof providing improved and/or more informative search results such that aneed to navigate through and/or download various pages of search resultsis decreased due to the improved and/or more informative search results,etc.).

Alternatively and/or additionally, implementation of at least some ofthe disclosed subject matter may lead to benefits including moreaccurate and precise identification and/or transmission of content tointended users (e.g., as a result of analyzing sequences of queries todetermine query sequence pairs, as a result of providing improved and/ormore informative query suggestions and/or search results based upon thequery sequence pairs and received keywords, etc.).

In some examples, at least some of the disclosed subject matter may beimplemented on a client device, and in some examples, at least some ofthe disclosed subject matter may be implemented on a server (e.g.,hosting a service accessible via a network, such as the Internet).

FIG. 6 is an illustration of a scenario 600 involving an examplenon-transitory machine readable medium 602. The non-transitory machinereadable medium 602 may comprise processor-executable instructions 612that when executed by a processor 616 cause performance (e.g., by theprocessor 616) of at least some of the provisions herein (e.g.,embodiment 614). The non-transitory machine readable medium 602 maycomprise a memory semiconductor (e.g., a semiconductor utilizing staticrandom access memory (SRAM), dynamic random access memory (DRAM), and/orsynchronous dynamic random access memory (SDRAM) technologies), aplatter of a hard disk drive, a flash memory device, or a magnetic oroptical disc (such as a compact disc (CD), digital versatile disc (DVD),or floppy disk). The example non-transitory machine readable medium 602stores computer-readable data 604 that, when subjected to reading 606 bya reader 610 of a device 608 (e.g., a read head of a hard disk drive, ora read operation invoked on a solid-state storage device), express theprocessor-executable instructions 612. In some embodiments, theprocessor-executable instructions 612, when executed, cause performanceof operations, such as at least some of the example method 400 of FIG.4, for example. In some embodiments, the processor-executableinstructions 612 are configured to cause implementation of a system,such as at least some of the example system 501 of FIGS. 5A-5F, forexample.

3. Usage of Terms

As used in this application, “component,” “module,” “system”,“interface”, and/or the like are generally intended to refer to acomputer-related entity, either hardware, a combination of hardware andsoftware, software, or software in execution. For example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration, both an application runningon a controller and the controller can be a component. One or morecomponents may reside within a process and/or thread of execution and acomponent may be localized on one computer and/or distributed betweentwo or more computers.

Unless specified otherwise, “first,” “second,” and/or the like are notintended to imply a temporal aspect, a spatial aspect, an ordering, etc.Rather, such terms are merely used as identifiers, names, etc. forfeatures, elements, items, etc. For example, a first object and a secondobject generally correspond to object A and object B or two different ortwo identical objects or the same object.

Moreover, “example” is used herein to mean serving as an instance,illustration, etc., and not necessarily as advantageous. As used herein,“or” is intended to mean an inclusive “or” rather than an exclusive“or”. In addition, “a” and “an” as used in this application aregenerally be construed to mean “one or more” unless specified otherwiseor clear from context to be directed to a singular form. Also, at leastone of A and B and/or the like generally means A or B or both A and B.Furthermore, to the extent that “includes”, “having”, “has”, “with”,and/or variants thereof are used in either the detailed description orthe claims, such terms are intended to be inclusive in a manner similarto the term “comprising”.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing at least some of the claims.

Furthermore, the claimed subject matter may be implemented as a method,apparatus, or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device, carrier, or media. Of course, manymodifications may be made to this configuration without departing fromthe scope or spirit of the claimed subject matter.

Various operations of embodiments are provided herein. In an embodiment,one or more of the operations described may constitute computer readableinstructions stored on one or more computer and/or machine readablemedia, which if executed will cause the operations to be performed. Theorder in which some or all of the operations are described should not beconstrued as to imply that these operations are necessarily orderdependent. Alternative ordering will be appreciated by one skilled inthe art having the benefit of this description. Further, it will beunderstood that not all operations are necessarily present in eachembodiment provided herein. Also, it will be understood that not alloperations are necessary in some embodiments.

Also, although the disclosure has been shown and described with respectto one or more implementations, equivalent alterations and modificationswill occur to others skilled in the art based upon a reading andunderstanding of this specification and the annexed drawings. Thedisclosure includes all such modifications and alterations and islimited only by the scope of the following claims. In particular regardto the various functions performed by the above described components(e.g., elements, resources, etc.), the terms used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., that is functionally equivalent), even though notstructurally equivalent to the disclosed structure. In addition, while aparticular feature of the disclosure may have been disclosed withrespect to only one of several implementations, such feature may becombined with one or more other features of the other implementations asmay be desired and advantageous for any given or particular application.

What is claimed is:
 1. A method, comprising: controlling a graphicaluser interface of a first device to display a search interface;receiving one or more keywords, via the search interface, from the firstdevice; determining, based upon the one or more keywords and ahistorical query database comprising a plurality of historical queries,a plurality of queries associated with the one or more keywords;generating, based upon a plurality of search sessions associated withthe plurality of historical queries, a plurality of relationship scoresassociated with the plurality of queries determined based upon thehistorical query database comprising the plurality of historicalqueries, wherein: a first search session of the plurality of searchsessions corresponds to one or more searches performed via a seconddevice using one or more queries of the plurality of historical queries;and a relationship score of the plurality of relationship scores isassociated with a relationship between a query of the plurality ofqueries and the one or more keywords; analyzing the historical querydatabase to determine a plurality of click rates associated with theplurality of queries; and generating, based upon the plurality ofqueries, the plurality of relationship scores and the plurality of clickrates, a list of suggested queries associated with the one or morekeywords.
 2. The method of claim 1, comprising: ranking the plurality ofqueries based upon the plurality of relationship scores and theplurality of click rates to generate a plurality of rankings associatedwith the plurality of queries, wherein the generating the list ofsuggested queries is performed based upon the plurality of rankings. 3.The method of claim 2, wherein the list of suggested queries comprises afirst suggested query above a second suggested query based upon a firstranking of the first suggested query being higher than a second rankingof the second suggested query.
 4. The method of claim 1, comprising:presenting, via the search interface, one or more suggested queries ofthe list of suggested queries.
 5. The method of claim 2, comprising:determining that one or more rankings associated with one or moresuggested queries of the list of suggested queries meet a rankthreshold; and presenting, via the search interface, the one or moresuggested queries.
 6. The method of claim 1, comprising: receiving afirst query, of the one or more queries associated with the first searchsession, from the second device during the first search session at afirst time; receiving a second query, of the one or more queriesassociated with the first search session, from the second device duringthe first search session at a second time after the first time; anddetermining a sequence of queries associated with the first searchsession based upon the first query and the second query, wherein: thesequence of queries is indicative of the second query being receivedafter the first query is received; the generating the plurality ofrelationship scores is performed based upon a plurality of sequences ofqueries associated with the plurality of search sessions; and theplurality of sequences of queries comprises the sequence of queries. 7.The method of claim 1, comprising: determining a first query pathassociated with the one or more keywords, wherein the first query pathcomprises a first query, of the plurality of queries, corresponding to afirst step of the first query path and one or more second queries, ofthe plurality of queries, corresponding to a second step of the firstquery path; and determining a second query path associated with the oneor more keywords, wherein the second query path comprises a third query,of the plurality of queries, corresponding to a first step of the secondquery path and one or more fourth queries, of the plurality of queries,corresponding to a second step of the second query path.
 8. The methodof claim 7, wherein the generating the list of suggested queriescomprises including the first query corresponding to the first step ofthe first query path and the third query corresponding to the first stepof the second query path in the list of suggested queries.
 9. The methodof claim 8, comprising: presenting at least a portion of the list ofsuggested queries comprising the first query corresponding to the firststep of the first query path and the third query corresponding to thefirst step of the second query path; receiving a selection of the firstquery corresponding to the first step of the first query path; andpresenting, based upon the selection of the first query corresponding tothe first step of the first query path, the one or more second queriescorresponding to the second step of the first query path.
 10. The methodof claim 1, comprising: removing queries from the plurality of queriesresponsive to determining that the queries are associated withrelationship scores that do not meet a threshold relationship score. 11.The method of claim 1, wherein the one or more keywords correspond to aquery, the method comprising: presenting, via the search interface, aplurality of search results corresponding to a plurality of web pagesassociated with the one or more keywords.
 12. The method of claim 1,wherein the one or more keywords correspond to a query, the methodcomprising: generating a first set of search results based upon the oneor more keywords; generating a second set of search results based uponthe list of suggested queries; and presenting, via the search interface,a plurality of search results corresponding to a plurality of web pages,wherein the plurality of search results comprises the first set ofsearch results associated with the one or more keywords and the secondset of search results associated with the list of suggested queries. 13.The method of claim 2, wherein the one or more keywords correspond to aquery, the method comprising: generating a plurality of search resultscorresponding to a plurality of web pages associated with the one ormore keywords; ranking the plurality of search results based upon atleast one of the plurality of rankings or the list of suggested queriesto generate a second plurality of rankings associated with the pluralityof search results; generating a ranked list of search results based uponthe plurality of search results and the second plurality of rankings;and presenting, via the search interface, the ranked list of searchresults.
 14. The method of claim 1, wherein a search session of theplurality of search sessions is associated with a sequence of queries,the method comprising: training a machine learning model using aplurality of sequences of queries associated with the plurality ofsearch sessions, wherein the plurality of sequences of queries areassociated with the one or more keywords.
 15. The method of claim 14,wherein the plurality of relationship scores is generated using themachine learning model.
 16. The method of claim 14, wherein the list ofsuggested queries is generated using the machine learning model.
 17. Anon-transitory machine readable medium having stored thereonprocessor-executable instructions that when executed cause performanceof operations, the operations comprising: controlling a graphical userinterface of a first device to display a search interface; receiving oneor more keywords, via the search interface, from the first device;determining, based upon the one or more keywords and a historical querydatabase comprising a plurality of historical queries, a plurality ofqueries associated with the one or more keywords; generating, based upona plurality of search sessions associated with the plurality ofhistorical queries, a plurality of relationship scores associated withthe plurality of queries determined based upon the historical querydatabase comprising the plurality of historical queries, wherein: afirst search session of the plurality of search sessions corresponds toone or more searches performed via a second device using one or morequeries of the plurality of historical queries; and a relationship scoreof the plurality of relationship scores is associated with arelationship between a query of the plurality of queries and the one ormore keywords; and generating, based upon the plurality of queries andthe plurality of relationship scores, a list of suggested queriesassociated with the one or more keywords.
 18. The non-transitory machinereadable medium of claim 17, the operations comprising: ranking theplurality of queries based upon the plurality of relationship scores togenerate a plurality of rankings associated with the plurality ofqueries, wherein the generating the list of suggested queries isperformed based upon the plurality of rankings.
 19. A computing devicecomprising: a processor; and memory comprising processor-executableinstructions that when executed by the processor cause performance ofoperations, the operations comprising: controlling a graphical userinterface of a first device to display a search interface; receiving oneor more keywords, via the search interface, from the first device;determining, based upon the one or more keywords and a historical querydatabase comprising a plurality of historical queries, a plurality ofqueries associated with the one or more keywords; generating, based upona plurality of search sessions associated with the plurality ofhistorical queries, a plurality of relationship scores associated withthe plurality of queries determined based upon the historical querydatabase comprising the plurality of historical queries, wherein: afirst search session of the plurality of search sessions corresponds toone or more searches performed via a second device using one or morequeries of the plurality of historical queries; and a relationship scoreof the plurality of relationship scores is associated with arelationship between a query of the plurality of queries and the one ormore keywords; analyzing the historical query database to determine aplurality of click rates associated with the plurality of queries; andgenerating, based upon the plurality of queries, the plurality ofrelationship scores and the plurality of click rates, a list ofsuggested queries associated with the one or more keywords.
 20. Thecomputing device of claim 19, the operations comprising: ranking theplurality of queries based upon the plurality of relationship scores andthe plurality of click rates to generate a plurality of rankingsassociated with the plurality of queries, wherein the generating thelist of suggested queries is performed based upon the plurality ofrankings.